1 citations · 2 across the 11 of their papers we have counts for
12 papers
Keep What Audio Cannot Say: Context-Preserving Token Pruning for Omni-LLMs
Chaeyoung Jung, Kyeongha Rho, Joon Son Chung
Omnimodal Large Language Models (Omni-LLMs) incur substantial computational overhead due to the large number of multimodal input tokens they process, making token reduction essenti…
Probing Cross-modal Information Hubs in Audio-Visual LLMs
Jihoo Jung, Chaeyoung Jung, Ji-Hoon Kim +1
Audio-visual large language models (AVLLMs) have recently emerged as a powerful architecture capable of jointly reasoning over audio, visual, and textual modalities. In AVLLMs, the…
FastAV: Efficient Token Pruning for Audio-Visual Large Language Model Inference
Chaeyoung Jung, Youngjoon Jang, Seungwoo Lee +1
In this work, we present FastAV, the first token pruning framework tailored for audio-visual large language models (AV-LLMs). While token pruning has been actively explored in stan…
InfiniteAudio: Infinite-Length Audio Generation with Consistency
Chaeyoung Jung, Hojoon Ki, Ji-Hoon Kim +2
This paper presents InfiniteAudio, a simple yet effective strategy for generating infinite-length audio using diffusion-based text-to-audio methods. Current approaches face memory…
SEED: Speaker Embedding Enhancement Diffusion Model
KiHyun Nam, Jungwoo Heo, Jee-weon Jung +4
A primary challenge when deploying speaker recognition systems in real-world applications is performance degradation caused by environmental mismatch. We propose a diffusion-based…
Fork-Merge Decoding: Enhancing Multimodal Understanding in Audio-Visual Large Language Models
Chaeyoung Jung, Youngjoon Jang, Jongmin Choi +1
The goal of this work is to enhance balanced multimodal understanding in audio-visual large language models (AV-LLMs) by addressing modality bias without additional training. In cu…
AVCD: Mitigating Hallucinations in Audio-Visual Large Language Models through Contrastive Decoding
Chaeyoung Jung, Youngjoon Jang, Joon Son Chung
Hallucination remains a major challenge in multimodal large language models (MLLMs). To address this, various contrastive decoding (CD) methods have been proposed that contrasts or…
From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speech
Ji-Hoon Kim, Jeongsoo Choi, Jaehun Kim +2
The objective of this study is to generate high-quality speech from silent talking face videos, a task also known as video-to-speech synthesis. A significant challenge in video-to-…
VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis
Jaemin Jung, Junseok Ahn, Chaeyoung Jung +3
We present VoiceDiT, a multi-modal generative model for producing environment-aware speech and audio from text and visual prompts. While aligning speech with text is crucial for in…
FlowAVSE: Efficient Audio-Visual Speech Enhancement with Conditional Flow Matching
Chaeyoung Jung, Suyeon Lee, Ji-Hoon Kim +1
This work proposes an efficient method to enhance the quality of corrupted speech signals by leveraging both acoustic and visual cues. While existing diffusion-based approaches hav…
Seeing Through the Conversation: Audio-Visual Speech Separation based on Diffusion Model
Suyeon Lee, Chaeyoung Jung, Youngjoon Jang +2
The objective of this work is to extract target speaker's voice from a mixture of voices using visual cues. Existing works on audio-visual speech separation have demonstrated their…
TalkNCE: Improving Active Speaker Detection with Talk-Aware Contrastive Learning
Chaeyoung Jung, Suyeon Lee, Kihyun Nam +4
The goal of this work is Active Speaker Detection (ASD), a task to determine whether a person is speaking or not in a series of video frames. Previous works have dealt with the tas…